OpenRaiser/Intern-Atlas huggingface dataset:
Crowd Simulation Modeling Applied to Emergency and Evacuation Simulations using Multi-Agent Systems
Venue: arXiv cs.MA 2013
Year: 2013

In recent years crowd modeling has become increasingly important both in the computer games industry and in emergency simulation. This paper discusses some aspects of what has been accomplished in this field, from social sciences to the computer implementation of modeling and simulation. Problem overview is described including some of the most common techniques used. Multi-Agent Systems is stated as the preferred approach for emergency evacuation simulations. A framework is proposed based on the work of Fangqin and Aizhu with extensions to include some BDI aspects. Future work includes expansion of the model's features and implementation of a prototype for validation of the propose methodology.
Delay and Delay Variation Constrained Algorithm based on VNS Algorithm for RP Management in Mobile IPv6
Venue: arXiv cs.NI 2013
Year: 2013

On account of the progress of network multimedia technology, more and more real-time multimedia applications arrive with the need to transmit information using multicast communication. These applications are more important with the arrival of mobile IPv6 protocol through mobile receivers and sources. These applications require a multicast routing protocol which has packets arriving at multicast receptors within a specified QoS guaranteed and a quick recovery mechanism. When multicasting with mobile IPv6, the mobility of a receivers and senders may lead to serious problems. When the receiver or sender moves, the quality of full multicast tree may degrade so that multicast datagrams cannot be forwarded efficiently. D2V-RPM (delay and delay variation RP Manager) problem consist in choosing an optimal multicast router in the network as the root of the shared multicast tree (ST) within a specified delay and delay variation bound for all multicast session, and recovering this RP if it's not optimal or it has failed. This NP hard problem needs to be solved through a heuristic algorithm. In this paper, we propose a new RP Management algorithm based on Variable Neighborhood Search algorithm, based on a systematic neighborhood changing. D2VVNS-RPM algorithm selects and recovers the RP router by considering tree cost, delay and delay variation. Simulation results show that good performance is achieved.
The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux
Venue: arXiv cs.CR 2013
Year: 2013

The increasing availability of cloud computing and scientific super computers brings great potential for making R accessible through public or shared resources. This allows us to efficiently run code requiring lots of cycles and memory, or embed R functionality into, e.g., systems and web services. However some important security concerns need to be addressed before this can be put in production. The prime use case in the design of R has always been a single statistician running R on the local machine through the interactive console. Therefore the execution environment of R is entirely unrestricted, which could result in malicious behavior or excessive use of hardware resources in a shared environment. Properly securing an R process turns out to be a complex problem. We describe various approaches and illustrate potential issues using some of our personal experiences in hosting public web services. Finally we introduce the RAppArmor package: a Linux based reference implementation for dynamic sandboxing in R on the level of the operating system.
Computation of the Travelling Salesman Problem by a Shrinking Blob
Venue: arXiv cs.ET 2013
Year: 2013

The Travelling Salesman Problem (TSP) is a well known and challenging combinatorial optimisation problem. Its computational intractability has attracted a number of heuristic approaches to generate satisfactory, if not optimal, candidate solutions. In this paper we demonstrate a simple unconventional computation method to approximate the Euclidean TSP using a virtual material approach. The morphological adaptation behaviour of the material emerges from the low-level interactions of a population of particles moving within a diffusive lattice. A `blob' of this material is placed over a set of data points projected into the lattice, representing TSP city locations, and the blob is reduced in size over time. As the blob shrinks it morphologically adapts to the configuration of the cities. The shrinkage process automatically stops when the blob no longer completely covers all cities. By manually tracing the perimeter of the blob a path between cities is elicited corresponding to a TSP tour. Over 6 runs on 20 randomly generated datasets of 20 cities this simple and unguided method found tours with a mean best tour length of 1.04, mean average tour length of 1.07 and mean worst tour length of 1.09 when expressed as a fraction of the minimal tour computed by an exact TSP solver. We examine the insertion mechanism by which the blob constructs a tour, note some properties and limitations of its performance, and discuss the relationship between the blob TSP and proximity graphs which group points on the plane. The method is notable for its simplicity and the spatially represented mechanical mode of its operation. We discuss similarities between this method and previously suggested models of human performance on the TSP and suggest possibilities for further improvement.
On the Behavior of RObust Header Compression U-mode in Channels with Memory
Venue: arXiv cs.NI 2013
Year: 2013

The existing studies of RObust Header Compression (ROHC) have provided some understanding for memoryless channel, but the behavior of ROHC for correlated wireless channels is not well investigated in spite of its practical importance. In this paper, the dependence of ROHC against its design parameters for the Gilbert Elliot channel is studied by means of three analytical models. A first more elaborated approach accurately predicts the behavior of the protocol for the single RTP flow profile, while a simpler, analytically tractable model yields clear and insightful mathematical relationships that explain the qualitative trends of ROHC. The results are validated against a real world implementation of this protocol. Moreover, a third model studies also the less conventional yet practically relevant setting of multiple RTP flows.
Compressive Shift Retrieval
Venue: arXiv cs.SY 2013
Year: 2013

The classical shift retrieval problem considers two signals in vector form that are related by a shift. The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals. Inspired by compressive sensing, in this paper, we seek to estimate the shift directly from compressed signals. We show that under certain conditions, the shift can be recovered using fewer samples and less computation compared to the classical setup. Of particular interest is shift estimation from Fourier coefficients. We show that under rather mild conditions only one Fourier coefficient suffices to recover the true shift.
Quantifying Social Network Dynamics
Venue: arXiv cs.SI 2013
Year: 2013

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.
Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving Object Trajectories
Venue: arXiv cs.AI 2013
Year: 2013

Several algorithms have been proposed for discovering patterns from trajectories of moving objects, but only a few have concentrated on outlier detection. Existing approaches, in general, discover spatial outliers, and do not provide any further analysis of the patterns. In this paper we introduce semantic spatial and spatio-temporal outliers and propose a new algorithm for trajectory outlier detection. Semantic outliers are computed between regions of interest, where objects have similar movement intention, and there exist standard paths which connect the regions. We show with experiments on real data that the method finds semantic outliers from trajectory data that are not discovered by similar approaches.
Representing Heuristic Knowledge in D-S Theory
Venue: arXiv cs.AI 2013
Year: 2013

The Dempster-Shafer theory of evidence has been used intensively to deal with uncertainty in knowledge-based systems. However the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major research problem. This paper presents an approach to representing such heuristic knowledge by evidential mappings which are defined on the basis of mass functions. The relationships between evidential mappings and multi valued mappings, as well as between evidential mappings and Bayesian multi- valued causal link models in Bayesian theory are discussed. Following this the detailed procedures for constructing evidential mappings for any set of heuristic rules are introduced. Several situations of belief propagation are discussed.
MESA: Maximum Entropy by Simulated Annealing
Venue: arXiv cs.AI 2013
Year: 2013

Probabilistic reasoning systems combine different probabilistic rules and probabilistic facts to arrive at the desired probability values of consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by Simulated Annealing) that derives a joint distribution of variables or propositions. It takes into account the reliability of probability values and can resolve conflicts between contradictory statements. The joint distribution is represented in terms of marginal distributions and therefore allows to process large inference networks and to determine desired probability values with high precision. The procedure derives a maximum entropy distribution subject to the given constraints. It can be applied to inference networks of arbitrary topology and may be extended into a number of directions.
Bottom-k and Priority Sampling, Set Similarity and Subset Sums with Minimal Independence
Venue: arXiv cs.DS 2013
Year: 2013

We consider bottom-k sampling for a set X, picking a sample S_k(X) consisting of the k elements that are smallest according to a given hash function h. With this sample we can estimate the relative size f=|Y|/|X| of any subset Y as |S_k(X) intersect Y|/k. A standard application is the estimation of the Jaccard similarity f=|A intersect B|/|A union B| between sets A and B. Given the bottom-k samples from A and B, we construct the bottom-k sample of their union as S_k(A union B)=S_k(S_k(A) union S_k(B)), and then the similarity is estimated as |S_k(A union B) intersect S_k(A) intersect S_k(B)|/k. We show here that even if the hash function is only 2-independent, the expected relative error is O(1/sqrt(fk)). For fk=Omega(1) this is within a constant factor of the expected relative error with truly random hashing. For comparison, consider the classic approach of kxmin-wise where we use k hash independent functions h_1,...,h_k, storing the smallest element with each hash function. For kxmin-wise there is an at least constant bias with constant independence, and it is not reduced with larger k. Recently Feigenblat et al. showed that bottom-k circumvents the bias if the hash function is 8-independent and k is sufficiently large. We get down to 2-independence for any k. Our result is based on a simply union bound, transferring generic concentration bounds for the hashing scheme to the bottom-k sample, e.g., getting stronger probability error bounds with higher independence. For weighted sets, we consider priority sampling which adapts efficiently to the concrete input weights, e.g., benefiting strongly from heavy-tailed input. This time, the analysis is much more involved, but again we show that generic concentration bounds can be applied.
Sorting in Lattices
Venue: arXiv cs.DM 2013
Year: 2013

In a totally ordered set the notion of sorting a finite sequence is defined through a suitable permutation of the sequence's indices. In this paper we prove a simple formula that explicitly describes how the elements of a sequence are related to those of its sorted counterpart. As this formula relies only on the minimum and maximum functions we use it to define the notion of sorting for lattices. A major difference of sorting in lattices is that it does not guarantee that sequence elements are only rearranged. However, we can show that other fundamental properties that are associated with sorting are preserved.
Cortical Surface Co-Registration based on MRI Images and Photos
Venue: arXiv cs.CV 2013
Year: 2013

Brain shift, i.e. the change in configuration of the brain after opening the dura mater, is a key problem in neuronavigation. We present an approach to co-register intra-operative microscope images with pre-operative MRI to adapt and optimize intra-operative neuronavigation. The tools are a robust classification of sulci on MRI extracted cortical surfaces, guided user marking of most prominent sulci on a microscope image, and the actual variational registration method with a fidelity energy for 3D deformations of the cortical surface combined with a higher order, linear elastica type prior energy. Furthermore, the actual registration is validated on an artificial testbed with known ground truth deformation and on real data of a neuro clinical patient.
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
Venue: arXiv cs.AI 2013
Year: 2013

This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so generated has been termed "intercausal." It is shown by construction that inter-causal independence is possible for binary distributions at one state of evidence. For such "CICI" distributions, the two measures of inter-causal effect, "multiplicative synergy" and "additive synergy" are equal. The well known "noisy-or" model is an example of such a distribution. This introduces novel semantics for the noisy-or, as a model of the degree of conflict among competing hypotheses of a common observation.
Constraint Propagation with Imprecise Conditional Probabilities
Venue: arXiv cs.AI 2013
Year: 2013

An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest from the knowledge of the bracketing of some other conditional probabilities. A procedure that uses two such propagation rules repeatedly is proposed in order to estimate any simple conditional probability of interest from the available knowledge. The iterative procedure, that does not require independence assumptions, looks promising with respect to the linear programming method. Improved bounds for conditional probabilities are given when independence assumptions hold.
Bayesian Networks Aplied to Therapy Monitoring
Venue: arXiv cs.AI 2013
Year: 2013

We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on the proposed class of models. As an illustration we present an application to the monitoring of cytotoxic chemotherapy in breast cancer.
A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency
Venue: arXiv cs.AI 2013
Year: 2013

A semantics is given to possibilistic logic, a logic that handles weighted classical logic formulae, and where weights are interpreted as lower bounds on degrees of certainty or possibility, in the sense of Zadeh's possibility theory. The proposed semantics is based on fuzzy sets of interpretations. It is tolerant to partial inconsistency. Satisfiability is extended from interpretations to fuzzy sets of interpretations, each fuzzy set representing a possibility distribution describing what is known about the state of the world. A possibilistic knowledge base is then viewed as a set of possibility distributions that satisfy it. The refutation method of automated deduction in possibilistic logic, based on previously introduced generalized resolution principle is proved to be sound and complete with respect to the proposed semantics, including the case of partial inconsistency.
Handling Uncertainty during Plan Recognition in Task-Oriented Consultation Systems
Venue: arXiv cs.AI 2013
Year: 2013

During interactions with human consultants, people are used to providing partial and/or inaccurate information, and still be understood and assisted. We attempt to emulate this capability of human consultants; in computer consultation systems. In this paper, we present a mechanism for handling uncertainty in plan recognition during task-oriented consultations. The uncertainty arises while choosing an appropriate interpretation of a user?s statements among many possible interpretations. Our mechanism handles this uncertainty by using probability theory to assess the probabilities of the interpretations, and complements this assessment by taking into account the information content of the interpretations. The information content of an interpretation is a measure of how well defined an interpretation is in terms of the actions to be performed on the basis of the interpretation. This measure is used to guide the inference process towards interpretations with a higher information content. The information content for an interpretation depends on the specificity and the strength of the inferences in it, where the strength of an inference depends on the reliability of the information on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The domain that we have chosen for exploration is that of a travel agency.
Structuring Bodies of Evidence
Venue: arXiv cs.AI 2013
Year: 2013

In this article we present two ways of structuring bodies of evidence, which allow us to reduce the complexity of the operations usually performed in the framework of evidence theory. The first structure just partitions the focal elements in a body of evidence by their cardinality. With this structure we are able to reduce the complexity on the calculation of the belief functions Bel, Pl, and Q. The other structure proposed here, the Hierarchical Trees, permits us to reduce the complexity of the calculation of Bel, Pl, and Q, as well as of the Dempster's rule of combination in relation to the brute-force algorithm. Both these structures do not require the generation of all the subsets of the reference domain.
A Fusion Algorithm for Solving Bayesian Decision Problems
Venue: arXiv cs.AI 2013
Year: 2013

This paper proposes a new method for solving Bayesian decision problems. The method consists of representing a Bayesian decision problem as a valuation-based system and applying a fusion algorithm for solving it. The fusion algorithm is a hybrid of local computational methods for computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems.
Detecting Causal Relations in the Presence of Unmeasured Variables
Venue: arXiv cs.AI 2013
Year: 2013

The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it is possible to reliably infer the causal relation between two measured variables, regardless of whether latent variables are acting or not.
A Method for Integrating Utility Analysis into an Expert System for Design Evaluation
Venue: arXiv cs.AI 2013
Year: 2013

In mechanical design, there is often unavoidable uncertainty in estimates of design performance. Evaluation of design alternatives requires consideration of the impact of this uncertainty. Expert heuristics embody assumptions regarding the designer's attitude towards risk and uncertainty that might be reasonable in most cases but inaccurate in others. We present a technique to allow designers to incorporate their own unique attitude towards uncertainty as opposed to those assumed by the domain expert's rules. The general approach is to eliminate aspects of heuristic rules which directly or indirectly include assumptions regarding the user's attitude towards risk, and replace them with explicit, user-specified probabilistic multi attribute utility and probability distribution functions. We illustrate the method in a system for material selection for automobile bumpers.
From Relational Databases to Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

The relationship between belief networks and relational databases is examined. Based on this analysis, a method to construct belief networks automatically from statistical relational data is proposed. A comparison between our method and other methods shows that our method has several advantages when generalization or prediction is deeded.
Similarity based Dynamic Web Data Extraction and Integration System from Search Engine Result Pages for Web Content Mining
Venue: arXiv cs.IR 2013
Year: 2013

There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall.
Ranking journals: Could Google Scholar Metrics be an alternative to Journal Citation Reports and Scimago Journal Rank?
Venue: arXiv cs.DL 2013
Year: 2013

The launch of Google Scholar Metrics as a tool for assessing scientific journals may be serious competition for Thomson Reuters Journal Citation Reports, and for Scopus powered Scimago Journal Rank. A review of these bibliometric journal evaluation products is performed. We compare their main characteristics from different approaches: coverage, indexing policies, search and visualization, bibliometric indicators, results analysis options, economic cost and differences in their ranking of journals. Despite its shortcomings, Google Scholar Metrics is a helpful tool for authors and editors in identifying core journals. As an increasingly useful tool for ranking scientific journals, it may also challenge established journals products
Geofence and Network Proximity
Venue: arXiv cs.NI 2013
Year: 2013

Many of modern location-based services are often based on an area or place as opposed to an accurate determination of the precise location. Geo-fencing approach is based on the observation that users move from one place to another and then stay at that place for a while. These places can be, for example, commercial properties, homes, office centers and so on. As per geo-fencing approach they could be described (defined) as some geographic areas bounded by polygons. It assumes users simply move from fence to fence and stay inside fences for a while. In this article we replace geo-based boundaries with network proximity rules. This new approach let us effectively deploy location based services indoor and provide a significant energy saving for mobile devices comparing with the traditional methods.